Biomarkers have transformed the diagnosis and treatment of cancer, cardiovascular disease, and a host of other medical conditions. In contrast, psychiatric biomarkers remain largely elusive, due in part to the fact that there is a weak correspondence between psychiatric diagnoses and their neurobiological substrates. This is especially true for depression and anxiety disorders, clinically heterogeneous conditions associated with abnormal reactivity to rewarding and aversive stimuli?and to the cues that predict them?and varied patterns of dysfunction in neural circuits that process positive and negative valence. This project will investigate how arousal processes interact with VS circuits to influence the anticipation and experience of rewarding and aversive stimuli in patients seeking treatment for symptoms of depression and anxiety, independent of conventional diagnostic categories. It is specifically designed to advance the goals of the Research Domain Criteria (RDoC) Project, by focusing on four related RDoC constructs that are directly germane to this question?arousal, potential threat threat, approach motivation, and reward attainment?and testing whether they explain variation in symptoms of anxiety and anhedonia in clinical populations. Importantly, this project will also advance RDoC's goal of developing new ways of classifying mental disorders, which will ultimately require methods for identifying novel diagnostic classes linked to homogeneous pathophysiology. To this end, we will test a strategy for discovering novel diagnostic subtypes defined by clustered patterns of abnormal functional connectivity in valence system circuits. We will use statistical clustering and machine learning methods to develop classifiers for diagnosing these subtypes in individual patients, and we will seek to identify distinct mechanisms by which atypical valence system reactivity may contribute to anxiety, anhedonia, and abnormal approach motivation and avoidance behavior in distinct patient subgroups, indexed in the laboratory via integrated, convergent measurements across multiple units of analysis.